Speaker "Anand Ranganathan" Details

Name :
anand ranganathan
Company :
Title :
VP, Data Products
Topic :

Data Science Out of The Box : Case Studies in the Telecommunications Industry

Abstract :

Telecommunications service providers (or telcos) have access to massive amounts of historical and streaming data about subscribers. However, it often takes them a long time to build, operationalize and gain value from various machine learning and analytic models. This is true even for relatively common use-cases like churn prediction, purchase propensity, next topup or purchase prediction, subscriber profiling, customer experience modeling, recommendation engines and fraud detection. In this talk, I shall describe our approach to tackling this problem, which involved having a pre-packaged set of analytic pipelines on a scalable Big Data architecture that work on several standard and well known telco data formats and sources, and that we were able to reuse across several different telcos. This allows the telcos to deploy the analytic pipelines on their data, out of the box, and go live in a matter of weeks, as opposed to the several months it used to take if they started from scratch. In the talk, I shall describe our experiences in deploying the pre-packaged analytic pipelines with several telcos in North America, South East Asia and the Middle East. The pipelines work on a variety of historical and streaming data, including call data records having voice, SMS and data usage information, purchase and recharge behavior, location information, browsing/clickstream data, billing and payment information, smartphone device logs, etc. The pipelines run on a combination of Spark and Unscrambl BRAIN, which includes a real-time machine learning framework, a scalable profile store based on Redis and an “aggregation engine” that stores efficient summaries of time-series data. I shall describe some of the machine learning models that get trained and scored as part of these pipelines. I shall also remark on how reusable certain models are across different telcos, and how a similar set of features can be used for models like next topup or purchase prediction, churn prediction and purchase propensity across similar telcos in different geographies.

Profile :

Anand Ranganathan is the VP of Solutions at Unscrambl, LLC, which is a startup building solutions incorporating a variety of Big Data platforms and real-time analytics for different industries. He is a data scientist, Big Data developer, architect and researcher rolled into one person. He has worked with over a 100 customers worldwide to design, implement and deploy Big Data and Real-time Analytics solutions, involving technologies like Kafka, IBM Streams, Hadoop, and Spark. He also has over 100 academic journal and conference publications and patent filings in his name in a variety of topics such as stream processing, data management, AI planning and knowledge representation, software engineering and the Semantic Web.


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